Ever since the concept of a process, or indeed any measurable activity, has existed there has been the desire to measure it and improve upon it. In the modern world we encounter processes everywhere from ordering a sandwich to the building of a car. Any of these processes can be graphically monitored by looking at some measure plotted against the relevant processes dimension, which is typically time, but could be any other dimension such as length.
In the 1950's a statistician called Deming took this idea to the manufacturing industry and showed that by applying a statistical rule to the data being displayed it was possible to show on the chart those points which were part of the normal variability of a process and those that were outside. These charts utilise three extra lines superimposed on top of the data, an average line and top and bottom process guidelines, the position of these lines on the chart being derived from the data rather than some arbitrary position. Significant events, by which we mean something out of the ordinary, are those points outside the process guidelines and by investigating and acting upon these; improvements in the performance of the process can be achieved. The charts are well known in the manufacturing industry as Statistical Process Control (SPC) charts and have been widely used in manufacturing since the early 1950's to great effect.
The problem faced by those seeking to implement SPC charts for a process within a business is that there is simply no enterprise wide, simple to configure, general purpose SPC tool that is relevant to every one in an organisation. To date, SPC charts and the software that displays them have remained in the domain of the statisticians and engineers looking after complex manufacturing processes.
In particular, the charts are limited by the lack of a mechanism to filter the charts by one or more dimensions, the representation of measure data that allows the charts to be aggregated and/or drilled down on, and a mechanism to efficiently supply a relevant chart when there is a large amount of data to analyse to any number of users in an organisation, and to insert new measures into the system once the system is in place.
In recent years the application of new IT systems such as ERP and CRM systems have provided firms with an increasing volume of data about their operations and as a result an increasing need to interpret and report on this data. This has resulted in the growth of a related set of technology focused on enterprise wide reporting. These systems include enterprise reporting systems and more specialist systems such as balanced scorecard reporting systems. All these systems seek to provide managers with information about the state of their operations.
These systems have a number of disadvantages. In particular, they require significant expense and effort to set up the data warehouse that most of these systems require and then to configure the reports and to provide ongoing support for the solutions. This in turn has generated its own industry of business intelligence systems support teams who spend their time creating and producing reports for management.
An additional problem that firms are discovering with these systems is that they create an increasing number of static, predefined reports which have to be continually refined to answer the specific questions that managers have about performance. What they cannot do is to allow users to investigate and understand their own performance in any way that they wish in real time because the reports are constrained to do just the job they were programmed to.
In view of this there would be considerable benefit for firms if they could go straight from their transactional data to interactive analysis of performance which allows them to investigate any aspect of performance that their data will support without the need to go through the intermediate stage of building a data warehouse and then defining KPI's and configuring and refining report formats.